Nose to brain delivery of nanostructured lipid carriers loaded with rivastigmine and nilotinib for treating Alzheimer’s disease: preparation, cell line study, and in vivo evaluation
Bibliographic record
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative that affects over 55 million people worldwide, a number expected to double by 2050 due to aging populations. This growing prevalence imposes immense societal and economic burdens on healthcare systems and caregivers. AD is challenging to treat with monotherapy, making combination therapy a more effective approach. This study focuses on delivering Rivastigmine tartrate (RIV), and Nilotinib hydrochloride monohydrate (NIL), to the brain to achieve synergistic effects against AD. The optimal ratio of the drug combination was determined using the combination index that was performed using the Neuro2a cells line. It was found to be 1:1, emphasizing the synergistic effect against the cell lines. So, nanostructured lipid carriers (NLCs) were loaded with RIV and NIL, both individually and in combination, developed and optimized in this study. The developed formulations were thoroughly characterized for globule size, polydispersity index (PDI), and entrapment efficiency (EE) for each drug and the combination. The globule size was > 200 nm, PDI > 0.3; EE < 85% in all the developed formulations. On performing an in vitro cell availability study it was found that developed NLCs showed a 1.3 to 1.4-fold increase in the viability of the cells. On conducting an in vivo study, the concentration in the brain following administration of different formulations was in the order of RIV-NIL-NLC > NIL-NLC > RIV-NLC > RIV-NIL SUS > NIL-SUS > RIV-SUS. There was a 3.5 to 5-fold increase in the concentration of RIV and NIL in the brain when administered as RIV-NIL-NLC. So, it can be concluded that the NLCs with combined drugs showed promising results, enhancing drug permeability through the intranasal route, therefore could be used for treating AD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".